1,674 research outputs found

    Role of the microbiome, probiotics, and 'dysbiosis therapy' in critical illness.

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    Purpose of reviewLoss of 'health-promoting' microbes and overgrowth of pathogenic bacteria (dysbiosis) in ICU is believed to contribute to nosocomial infections, sepsis, and organ failure (multiple organ dysfunction syndrome). This review discusses new understanding of ICU dysbiosis, new data for probiotics and fecal transplantation in ICU, and new data characterizing the ICU microbiome.Recent findingsICU dysbiosis results from many factors, including ubiquitous antibiotic use and overuse. Despite advances in antibiotic therapy, infections and mortality from often multidrug-resistant organisms (i.e., Clostridium difficile) are increasing. This raises the question of whether restoration of a healthy microbiome via probiotics or other 'dysbiosis therapies' would be an optimal alternative, or parallel treatment option, to antibiotics. Recent clinical data demonstrate probiotics can reduce ICU infections and probiotics or fecal microbial transplant (FMT) can treat Clostridium difficile. This contributes to recommendations that probiotics should be considered to prevent infection in ICU. Unfortunately, significant clinical variability limits the strength of current recommendations and further large clinical trials of probiotics and FMT are needed. Before larger trials of 'dysbiosis therapy' can be thoughtfully undertaken, further characterization of ICU dysbiosis is needed. To addressing this, we conducted an initial analysis demonstrating a rapid and marked change from a 'healthy' microbiome to an often pathogen-dominant microbiota (dysbiosis) in a broad ICU population.SummaryA growing body of evidence suggests critical illness and ubiquitous antibiotic use leads to ICU dysbiosis that is associated with increased ICU infection, sepsis, and multiple organ dysfunction syndrome. Probiotics and FMT show promise as ICU therapies for infection. We hope future-targeted therapies using microbiome signatures can be developed to correct 'illness-promoting' dysbiosis to restore a healthy microbiome post-ICU to improve patient outcomes

    Translational medicine and the human microbiome

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    Towards large-cohort comparative studies to define the factors influencing the gut microbial community structure of ASD patients.

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    Differences in the gut microbiota have been reported between individuals with autism spectrum disorders (ASD) and neurotypical controls, although direct evidence that changes in the microbiome contribute to causing ASD has been scarce to date. Here we summarize some considerations of experimental design that can help untangle causality in this complex system. In particular, large cross-sectional studies that can factor out important variables such as diet, prospective longitudinal studies that remove some of the influence of interpersonal variation in the microbiome (which is generally high, especially in children), and studies transferring microbial communities into germ-free mice may be especially useful. Controlling for the effects of technical variables, which have complicated efforts to combine existing studies, is critical when biological effect sizes are small. Large citizen-science studies with thousands of participants such as the American Gut Project have been effective at uncovering subtle microbiome effects in self-collected samples and with self-reported diet and behavior data, and may provide a useful complement to other types of traditionally funded and conducted studies in the case of ASD, especially in the hypothesis generation phase

    Type 1 diabetes and physical activity: An assessment of knowledge and needs in healthcare practitioners

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    This study examined healthcare practitioners’ knowledge and confidence in providing physical activity guidance to people with type 1 diabetes. Data collection occurred in the form of a 23-question, open-ended survey and semi-structured interviews exploring practitioners’ knowledge regarding exercise and type 1 diabetes. Participants had rarely received formal training regarding physical activity for people with type 1 diabetes. They indicated limited knowledge of specific physical activity guidelines, either for the general population or for people with type 1 diabetes. However, participants reported feeling relatively confident in their ability to advise people with type 1 diabetes regarding physical activity. The disparity between practitioners’ knowledge and confidence in advising people with type 1 diabetes about physical activity raises concerns regarding the accuracy of the information being provided to individuals with the condition

    Do universal codon-usage patterns minimize the effects of mutation and translation error?

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    BACKGROUND: Do species use codons that reduce the impact of errors in translation or replication? The genetic code is arranged in a way that minimizes errors, defined as the sum of the differences in amino-acid properties caused by single-base changes from each codon to each other codon. However, the extent to which organisms optimize the genetic messages written in this code has been far less studied. We tested whether codon and amino-acid usages from 457 bacteria, 264 eukaryotes, and 33 archaea minimize errors compared to random usages, and whether changes in genome G+C content influence these error values. RESULTS: We tested the hypotheses that organisms choose their codon usage to minimize errors, and that the large observed variation in G+C content in coding sequences, but the low variation in G+U or G+A content, is due to differences in the effects of variation along these axes on the error value. Surprisingly, the biological distribution of error values has far lower variance than randomized error values, but error values of actual codon and amino-acid usages are actually greater than would be expected by chance. CONCLUSION: These unexpected findings suggest that selection against translation error has not produced codon or amino-acid usages that minimize the effects of errors, and that even messages with very different nucleotide compositions somehow maintain a relatively constant error value. They raise the question: why do all known organisms use highly error-minimizing genetic codes, but fail to minimize the errors in the mRNA messages they encode

    UniFrac – An online tool for comparing microbial community diversity in a phylogenetic context

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    BACKGROUND: Moving beyond pairwise significance tests to compare many microbial communities simultaneously is critical for understanding large-scale trends in microbial ecology and community assembly. Techniques that allow microbial communities to be compared in a phylogenetic context are rapidly gaining acceptance, but the widespread application of these techniques has been hindered by the difficulty of performing the analyses. RESULTS: We introduce UniFrac, a web application available at , that allows several phylogenetic tests for differences among communities to be easily applied and interpreted. We demonstrate the use of UniFrac to cluster multiple environments, and to test which environments are significantly different. We show that analysis of previously published sequences from the Columbia river, its estuary, and the adjacent coastal ocean using the UniFrac interface provided insights that were not apparent from the initial data analysis, which used other commonly employed techniques to compare the communities. CONCLUSION: UniFrac provides easy access to powerful multivariate techniques for comparing microbial communities in a phylogenetic context. We thus expect that it will provide a completely new picture of many microbial interactions and processes in both environmental and medical contexts

    Porting and optimizing UniFrac for GPUs

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    UniFrac is a commonly used metric in microbiome research for comparing microbiome profiles to one another ("beta diversity"). The recently implemented Striped UniFrac added the capability to split the problem into many independent subproblems and exhibits near linear scaling. In this paper we describe steps undertaken in porting and optimizing Striped Unifrac to GPUs. We reduced the run time of computing UniFrac on the published Earth Microbiome Project dataset from 13 hours on an Intel Xeon E5-2680 v4 CPU to 12 minutes on an NVIDIA Tesla V100 GPU, and to about one hour on a laptop with NVIDIA GTX 1050 (with minor loss in precision). Computing UniFrac on a larger dataset containing 113k samples reduced the run time from over one month on the CPU to less than 2 hours on the V100 and 9 hours on an NVIDIA RTX 2080TI GPU (with minor loss in precision). This was achieved by using OpenACC for generating the GPU offload code and by improving the memory access patterns. A BSD-licensed implementation is available, which produces a C shared library linkable by any programming language.Comment: 4 pages, 3 figures, 4 table
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